Learning Phonological Mappings by Learning Strictly Local Functions

Jane Chandlee, Adam Jardine

Abstract


In this paper we identify strict locality as a defining computational property of the input-output mapping that underlies local phonological processes. We provide an automata-theoretic characterization for the class of Strictly Local functions, which are based on the well-studied Strictly Local formal languages (McNaughton & Papert 1971; Rogers & Pullum 2011; Rogers et al. 2013), and show how they can model a range of phonological processes. We then present a learning algorithm, the SLFLA, which uses the defining property of strict locality as an inductive principle to learn these mappings from finite data. The algorithm is a modification of an algorithm developed by Oncina et al. (1993) (called OSTIA) for learning the class of subsequential functions, of which the SL functions are a proper subset. We provide a proof that the SLFLA learns the class of SL functions and discuss these results alongside previous studies on using OSTIA to learn phonological mappings (Gildea and Jurafsky 1996).


Keywords


Phonological Processes; Learning Algorithm; Computational Property; Strict Locality

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DOI: http://dx.doi.org/10.3765/amp.v1i1.13

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